Transfer learning and pre-training tasks. Event representations and RGB images share some commonalities, especially edges and corners. However, if we compare them closely, we find subtle differences, since event-cameras recordings are highly correlated to the dynamic of the scene. If the RGB/event-frames analogy held, event-based vision could benefit widely from advancements in standard computer vision. As an example, recent computer vision research provides strong evidence in support of transfer learning from large dataset, e.g., the ImageNet dataset [1]}, [2]}. Further works explore and validate the correlation between 3D Human Pose Estimation and reconstruction tasks [3]}. These insights are supported by common intuition, as both tasks involve an understanding of the structure of the scene. Despite the differences between event and standard cameras, recent works validate the transfer learning hypothesis from RGB to constant-count representation [4]} and learnable representations [5]}. Moreover, Rebecq provide evidence for direct transfer learning by predicting natural images from spatio-temporal event-frames [6]}.
